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Time, Energy an‎d Priority Aware Tasks Scheduling in IoT, Fog Network using MFO Algorithm

مهندسي معماري كامپيوتر

With the rapid an‎d widespread growth of Internet of Things (IoT) devices, a wide range of applications have emerged that require real-time processing an‎d extremely low latency for effective performance. In recent years, cloud computing has been recognized as one of the primary approaches for data processing an‎d storage, providing diverse services on a large scale. However, the centralized nature of cloud computing poses limitations for IoT applications, as many of these applications deman‎d real-time processing an‎d low latency. To address these challenges, fog computing has been introduced as an efficient paradigm for processing IoT applications. Despite its advantages, fog computing devices are often distributed an‎d resource-constrained. Therefore, the optimal deployment of fog computing resources an‎d efficient task scheduling using appropriate algorithms remains a significant challenge. The base paper of this research employed a priority-aware semi-greedy algorithm an‎d its enhanced version, making valuable contributions to IoT task scheduling in fog environments. However, in fog computing, the use of metaheuristic algorithms can provide greater potential for finding optimal solutions. Accordingly, this study adopts the Moth-Flame Optimization (MFO) algorithm. By creating a balance between exploration an‎d exploitation, high flexibility, an‎d the ability to escape local optima, MFO serves as a complementary approach to previous methods an‎d can improve the quality of task scheduling. The performance of the MFO algorithm was eva‎luated in terms of reducing total energy consumption, minimizing total violation time, an‎d increasing the percentage of tasks meeting their deadlines. The experimental results demonstrated that the proposed algorithm significantly improves scheduling performance. The comparison between MFO an‎d the enhanced version of the priority-aware semi-greedy algorithm showed that both approaches improved efficiency an‎d performance in fog computing networks. However, MFO outperformed due to its effective balance between exploration an‎d exploitation an‎d its ability to avoid local optima. Based on the results, the MFO algorithm achieved a reduction in energy consumption by 20% to 45% compared to semi-greedy algorithms such as PSG an‎d PSG-M. Keywords: Internet of Things (IoT), Cloud Computing, Fog Computing, Task Scheduling, Moth-Flame Optimization Algorithm, Semi-Greedy Approach, Metaheuristic Approach.

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150107